Inferring cognitive wellness from motor patterns
Changes in the motor pattern have been shown to be useful advanced indicators of cognitive disorders, such as Parkinson's disease (PD) and cerebral small vessel disease (SVD). It would be highly advantageous to tap into data containing people's motor patterns from motion sensing devices to...
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sg-ntu-dr.10356-1391102020-05-15T07:54:27Z Inferring cognitive wellness from motor patterns Chen, Yiqiang Hu, Chunyu Hu, Bin Hu, Lisha Yu, Han Miao, Chunyan School of Computer Science and Engineering Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY) Engineering::Computer science and engineering Correlation Analysis Motor Pattern Changes in the motor pattern have been shown to be useful advanced indicators of cognitive disorders, such as Parkinson's disease (PD) and cerebral small vessel disease (SVD). It would be highly advantageous to tap into data containing people's motor patterns from motion sensing devices to analyze subtle changes in cognitive abilities, thereby providing personalized interventions before the actual onset of such conditions. However, this goal is very challenging due to two main technical problems: 1) the size of data labeled by doctors is small, and 2) the available data tends to be highly imbalanced (the vast majority tend to be from normal subjects with only a small fraction from subjects with cognitive disorder). In order to effectively deal with these challenges to infer cognitive wellness from motor patterns with high accuracy, we propose the MOtor-Cognitive Analytics (MOCA) framework. The proposed MOCA first uses the random oversampling iterative random forest based feature selection method to reduce the feature space dimensionality and avoid overfitting, and then adds a bias in the optimization problem of weighted extreme learning machine to achieve good generalization ability in handling imbalanced small-sampling dataset. Experimental results on two real-world datasets including SVD and stroke patients show that MOCA can effectively reduce the rate of misdiagnosis and significantly outperform state-of-the-art methods in inferring people's cognitive capabilities. This work opens up opportunities for population-level pre-screening using motion sensing devices and can inform current discussions on reforming the health-care infrastructure. NRF (Natl Research Foundation, S’pore) MOH (Min. of Health, S’pore) Accepted version 2020-05-15T07:54:27Z 2020-05-15T07:54:27Z 2018 Journal Article Chen, Y., Hu, C., Hu, B., Hu, L., Yu, H., & Miao, C. (2018). Inferring cognitive wellness from motor patterns. IEEE Transactions on Knowledge and Data Engineering, 30(12), 2340-2353. doi:10.1109/tkde.2018.2820024 1041-4347 https://hdl.handle.net/10356/139110 10.1109/TKDE.2018.2820024 2-s2.0-85044871818 12 30 2340 2353 en IEEE Transactions on Knowledge and Data Engineering © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TKDE.2018.2820024 application/pdf |
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Engineering::Computer science and engineering Correlation Analysis Motor Pattern Chen, Yiqiang Hu, Chunyu Hu, Bin Hu, Lisha Yu, Han Miao, Chunyan Inferring cognitive wellness from motor patterns |
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Changes in the motor pattern have been shown to be useful advanced indicators of cognitive disorders, such as Parkinson's disease (PD) and cerebral small vessel disease (SVD). It would be highly advantageous to tap into data containing people's motor patterns from motion sensing devices to analyze subtle changes in cognitive abilities, thereby providing personalized interventions before the actual onset of such conditions. However, this goal is very challenging due to two main technical problems: 1) the size of data labeled by doctors is small, and 2) the available data tends to be highly imbalanced (the vast majority tend to be from normal subjects with only a small fraction from subjects with cognitive disorder). In order to effectively deal with these challenges to infer cognitive wellness from motor patterns with high accuracy, we propose the MOtor-Cognitive Analytics (MOCA) framework. The proposed MOCA first uses the random oversampling iterative random forest based feature selection method to reduce the feature space dimensionality and avoid overfitting, and then adds a bias in the optimization problem of weighted extreme learning machine to achieve good generalization ability in handling imbalanced small-sampling dataset. Experimental results on two real-world datasets including SVD and stroke patients show that MOCA can effectively reduce the rate of misdiagnosis and significantly outperform state-of-the-art methods in inferring people's cognitive capabilities. This work opens up opportunities for population-level pre-screening using motion sensing devices and can inform current discussions on reforming the health-care infrastructure. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Chen, Yiqiang Hu, Chunyu Hu, Bin Hu, Lisha Yu, Han Miao, Chunyan |
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Article |
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Chen, Yiqiang Hu, Chunyu Hu, Bin Hu, Lisha Yu, Han Miao, Chunyan |
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Chen, Yiqiang |
title |
Inferring cognitive wellness from motor patterns |
title_short |
Inferring cognitive wellness from motor patterns |
title_full |
Inferring cognitive wellness from motor patterns |
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Inferring cognitive wellness from motor patterns |
title_full_unstemmed |
Inferring cognitive wellness from motor patterns |
title_sort |
inferring cognitive wellness from motor patterns |
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2020 |
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https://hdl.handle.net/10356/139110 |
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1681056646806110208 |